Using fuzzy logic to handle the semantic descriptions of music in a content-based retrieval system

This paper explores the potential use of fuzzy logic for semantic music recommendation. We show that a set of affective/emotive, structural and kinaesthetic descriptors can be used to formulate a query which allows the retrieval of intended music. A semantic music recommendation system was built, based on an elaborate study of potential users and an analysis of the semantic descriptors that best characterize the user’s understanding of music. Significant relationships between expressive and structural semantic descriptions of music were found. Fuzzy logic was then applied to handle the quality ratings associated with the semantic descriptions. A working semantic music recommendation system was tested and evaluated. Real-world testing revealed high user satisfaction

Using fuzzy logic to handle the users' semantic descriptions in a music retrieval system

This paper provides an investigation of the potential application of fuzzy logic to semantic music recommendation. We show that a set of affective/emotive, structural and kinaesthetic descriptors can be used to formulate a query which allows the retrieval of intended music. A semantic music recommendation system was built, based on an elaborate study of potential users of music information retrieval systems. In this study analysis was made of the descriptors that best characterize the user's understanding of music. Significant relationships between expressive and structural descriptions of music were found. A straightforward fuzzy logic methodology was then applied to handle the quality ratings associated with the descriptions. Rigorous real-world testing of the semantic music recommendation system revealed high user satisfaction

The Early Bird Catches The Term: Combining Twitter and News Data For Event Detection and Situational Awareness

Twitter updates now represent an enormous stream of information originating from a wide variety of formal and informal sources, much of which is relevant to real-world events. In this paper we adapt existing bio-surveillance algorithms to detect localised spikes in Twitter activity corresponding to real events with a high level of confidence. We then develop a methodology to automatically summarise these events, both by providing the tweets which fully describe the event and by linking to highly relevant news articles. We apply our methods to outbreaks of illness and events strongly affecting sentiment. In both case studies we are able to detect events verifiable by third party sources and produce high quality summaries

Sarcasm detection on Twitter

State-of-the-art approaches for sarcasm detection in social media combine lexical clues with contextual information surrounding the potentially sarcastic posting including author information. This article presents detailed methods for performing contextualizing sarcasm detection on Twitter, including data extraction, feature engineering and classification model settings. I reproduce the state-of-the-art results reported by Bamman and Smith (2015).Informatio